What are the biggest barriers to get RL in production?
There is a relatively recent paper that tackles this issue: Challenges of real-world reinforcement learning (2019) by Gabriel Dulac-Arnold et al., which presents all the challenges that need to be addressed to productionize RL to real world problems, the current approaches/solutions to solve the challenges, and metrics to evaluate them. I will only list them (based on the notes I had taken a few weeks ago). You should read the paper for more details. In any case, for people that are familiar with RL, they will be quite obvious.

Batch off-line and off-policy training

One current solution is importance sampling


Learning on the real system from limited samples (sample inefficiency)

Solutions: MAML, use expert demonstrations to bootstrap the agent, model-based approaches


High dimensional continuous state and action spaces

Solutions: AE-DQN, DRRN


Satisfying safety constraints

Solutions: constrained MDP, safe exploration strategies, etc.


Partial observability and non-stationarity

Solutions to partial observability: incorporate history in the observation, recurrent neural networks, etc.
Solutions to non-stationarity: domain randomization or system identification


Unspecified and multi-objective reward functions

Solutions: CVaR, Distributional DQN


Explainability
Real-time inference
System delays (see also this and this answers)

There's also a more recent and related paper An empirical investigation of the challenges of real-world reinforcement learning (2020) by Gabriel Dulac-Arnold et al, and here you have the associated code with the experiments.
However, note that RL (in particular, bandits) is already being used to solve at least one real-world problem [1, 2]. See also this answer.